Several approaches have been developed for computationally processing and responding to a human input, such as might be provided by a user to a web-based search service, or to an interface of an expert system. These approaches can be considered either semantic or statistical in nature.
For example, if one considers a typical sentence, such as “Smith hit the basket every time”, this sentence can be analyzed using a semantic approach by considering the meaning and relationship of the various words within the sentence. However, although this approach may seem intuitive to a human, and can provide accurate results, such approach is relatively computationally expensive to implement.
Alternatively, the sentence can be analyzed using a statistical approach by considering the occurrence of each word in the sentence, and, using a reference information culled from previous analysis of perhaps thousands of documents on various topics, compare the occurrence of the words within the sentence with weighted entries in that reference information. However, although the statistical approach is relatively computationally inexpensive to implement (through the use of lookups etc.), it typically is not as accurate as the semantic approach, and has a tendency to generate more false positives, such as incorrectly determining that a particular sentence pertains to a particular topic.
For example, in the above sentence, the word “basket” can pertain to the topic of basketball, or the topic of arts and crafts, and, while a semantic approach may successfully tackle such a sentence, a statistical approach may not satisfactorily resolve the topic, which in turn can lead to undesired results. This is the general area that embodiments of the invention are intended to address.